TY - GEN
T1 - When the Few Outweigh the Many
T2 - 20th International Conference on Security and Cryptography, SECRYPT 2023
AU - Cascavilla, G.
AU - Catolino, G.
AU - Conti, M.
AU - Mellios, D.
AU - Tamburri, D. A.
N1 - Publisher Copyright:
© 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).
PY - 2023
Y1 - 2023
N2 - The anonymity and untraceability benefits of the Dark web account for the exponentially-increased potential of its popularity while creating a suitable womb for many illicit activities, to date. Hence, in collaboration with cybersecurity and law enforcement agencies, research has provided approaches for recognizing and classifying illicit activities with most exploiting textual dark web markets’ content recognition; few such approaches use images that originated from dark web content. This paper investigates this alternative technique for recognizing illegal activities from images. In particular, we investigate label-agnostic learning techniques like One-Shot and Few-Shot learning featuring the use Siamese neural networks, a state-of-the-art approach in the field. Our solution manages to handle small-scale datasets with promising accuracy. In particular, Siamese neural networks reach 90.9% on 20-Shot experiments over a 10-class dataset; this leads us to conclude that such models are a promising and cheaper alternative to the definition of automated law-enforcing machinery over the dark web.
AB - The anonymity and untraceability benefits of the Dark web account for the exponentially-increased potential of its popularity while creating a suitable womb for many illicit activities, to date. Hence, in collaboration with cybersecurity and law enforcement agencies, research has provided approaches for recognizing and classifying illicit activities with most exploiting textual dark web markets’ content recognition; few such approaches use images that originated from dark web content. This paper investigates this alternative technique for recognizing illegal activities from images. In particular, we investigate label-agnostic learning techniques like One-Shot and Few-Shot learning featuring the use Siamese neural networks, a state-of-the-art approach in the field. Our solution manages to handle small-scale datasets with promising accuracy. In particular, Siamese neural networks reach 90.9% on 20-Shot experiments over a 10-class dataset; this leads us to conclude that such models are a promising and cheaper alternative to the definition of automated law-enforcing machinery over the dark web.
KW - Cybersecurity
KW - Dark Web
KW - Few-Shot Learning
KW - One-Shot Learning
KW - Siamese Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85178616755&partnerID=8YFLogxK
U2 - 10.5220/0012049400003555
DO - 10.5220/0012049400003555
M3 - Conference contribution
AN - SCOPUS:85178616755
SN - 9789897586668
T3 - Proceedings of the International Conference on Security and Cryptography
SP - 324
EP - 334
BT - SECRYPT 2023 - Proceedings of the 20th International Conference on Security and Cryptography
A2 - De Capitani di Vimercati, Sabrina
A2 - Samarati, Pierangela
PB - Science and Technology Publications, Lda
Y2 - 10 July 2023 through 12 July 2023
ER -